The global life insurance and retirement industry is facing an inflection point due to the convergence of challenging economic, technological, competitive and societal headwinds. Product-driven business models of the past will not be sustainable because insurers cannot adapt quickly enough to changing customer needs. This problem is on top of mature markets, strict regulatory requirements, low interest rates and tight margins. The COVID-19 pandemic has made it even more urgent for life insurers to redefine their role, take bold measures and address these changes.
The good news is that many global insurance leaders are already making large investments in digitization, innovation and cultural change. Going digital has been a top priority, as it helps reduce cost and enhances customer experiences, leading to the increasing adoption of predictive analytics, artificial intelligence (AI) and automation in various business functions in the industry. According to McKinsey estimates, the potential total value of AI and analytics across the insurance vertical is approximately $1.1 trillion.
Soon, AI will be deeply embedded into the insurance value chain, providing unmatched power to insurers: automating manual processes in underwriting, eliminating errors and inefficiencies in claims processing and enabling predictive insights to deliver superior outcomes. Below are the top challenges that AI and machine learning (ML) will help solve in the insurance industry.
1. Underwriting and Pricing — While pricing personal auto policies is mostly automated today, the underwriting process is still manual for commercial property. For commercial property insurance, the underwriter needs a lot of information, such as occupancy, data on adjacent buildings, loss estimates and typical hazards. Some of the data may be available online but may be outdated and might require onsite verification. This is why human judgment is critical. A PwC report on top insurance issues noted that carriers are devoting considerable attention to helping underwriters use models and AI-driven tools to supplement their knowledge. Underwriters are becoming increasingly comfortable marrying what they’ve learned from personal experience with insight from models to make the most informed decisions possible. Soon, underwriting will be fully automated, supported by machine learning models that ingest vast amounts of data through an ecosystem of vendors.
2. Claims Processing — In the future, machine learning algorithms will manage claims routing, increasing efficiency and accuracy dramatically. According to a McKinsey report, claims for personal lines and small-business insurance will be fully automated, enabling carriers to achieve straight-through-processing rates of more than 90% and dramatically reducing processing times from days to hours or minutes. Unlike with the traditional practice, involving manual methods of first notice of loss, the burden will no longer be on the customer to inform the insurance carrier about an event. The process will now be automated, relying on IoT sensors and real-time monitoring to prevent incidents from happening and sending notifications for critical events requiring immediate attention. An app on a smartphone will handle all interactions, with the capability to trigger claims automatically upon loss. Other technologies will support claims processing, such as natural language processing, deep learning and text analytics.
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3. Fraud Detection — Insurance fraud can cost companies millions to billions of dollars, as there are thousands of claims filed every day. Assigning insurance agents to investigate each case will be time-consuming and expensive. Using AI, insurers can evaluate millions of documents and data points in record time. They can cross-reference several databases and incorporate multiple external data sources, which would be impossible without automation. Anomaly detection models can identify deviations and flag cases for review. Leveraging learnings from previous fraud cases and using real-time data, AI and ML models can identify threat signals before they might become a more substantial problem.
4. Other Use Cases — A common use case is using predictive analytics for estimating policy cancellations. Customer churn is one of the most problematic aspects of customer management for insurance companies. When high-value customers churn, insurance companies often replace existing businesses with new, more costly customers that lower profitability. Creating AI and ML models that can accurately forecast churn behavior can boost profitability and revenues.
As insurance carriers get better at leveraging data and implementing predictive analytics, the focus will shift from product-led to customer-centric models. The insurance industry’s adoption and investment in digital capabilities to unify data, advanced analytics and people will ultimately make the industry more agile, efficient and transparent. The winners that go above and beyond will start to offer personalized products based on individual customers’ unique needs and enhanced customer experience.
